Unit 4: Building a Social Network Connection System

Overview

This Skillable lab activity requires you to implement a social network system that uses graphs to represent user connections, find mutual friends, discover connection paths between users, and recommend connections.

Learning Objectives

• Implement graph data structures using adjacency list representation
• Implement BFS traversal algorithm for finding paths
• Find mutual friends using graph intersection techniques
• Implement friend recommendation algorithm based on mutual connections
• Optimize solutions for handling large user bases efficiently
• Use Github Copilot effectively for code generation and exploration

Context

You're building a social network system that needs to:

• Represent user connections using a graph structure (undirected graph)
• Find paths between users using graph traversal algorithms
• Identify mutual friends between users
• Recommend connections based on mutual friends and graph analysis
• Handle large user bases efficiently

Project Structure

src/main/java/com/jse/socialnetwork/
├── User.java                  # User data class
├── SocialNetwork.java         # Main graph implementation
└── FriendRecommendation.java  # Recommendation algorithm

src/test/java/com/jse/socialnetwork/
└── SocialNetworkTest.java    # Test cases

Requirements

1. Graph Structure Implementation

• Use adjacency list representation for the graph
• Implement undirected graph (friendships are bidirectional)
• Support adding users and connections
• Efficient neighbor lookup

2. BFS Path Finding

• Implement BFS traversal algorithm
• Find shortest path between two users
• Handle disconnected users (no path exists)
• Return path as list of user IDs

3. Mutual Friends

• Find all mutual friends between two users
• Use graph intersection techniques
• Return list of mutual friend user IDs
• Handle cases where users have no mutual friends

4. Friend Recommendations

• Recommend friends based on mutual connections
• Consider users who are friends of friends
• Rank recommendations by number of mutual friends
• Return top N recommendations

5. Performance Optimization

• Efficient graph representation (adjacency list)
• Optimized BFS implementation
• Efficient mutual friend calculation
• Memory management for large networks

6. Github Copilot Integration

• Use Copilot to explore implementation options
• Generate helper methods with Copilot
• Validate and understand Copilot suggestions
• Document Copilot usage in code comments

Implementation Steps

1. Implement graph structure - Create adjacency list representation
2. Implement BFS traversal - Find paths between users
3. Find mutual friends - Use graph intersection
4. Implement recommendations - Algorithm based on mutual connections
5. Use Github Copilot - Explore options and generate code
6. Optimize performance - Apply optimization techniques
7. Test and validate - Run tests and validate Copilot suggestions

Testing

Run the provided test cases:

mvn test

Resources

• Social network framework provided
• User connection dataset
• System requirements document
• Github Copilot access
• Code examples for reference